# Nmr Metabolomics Profiling Workflow

> Use when you have NMR metabolomics data (1D/2D spectra or FIDs) and want a quantified, identified metabolite profile — spectral preprocessing (phase/baseline/referencing, binning), metabolite identification by chemical shift, quantification, and group statistics.

- Skill: `holobiomicslab/nmr-metabolomics-profiling-workflow` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add holobiomicslab/nmr-metabolomics-profiling-workflow`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/nmr-metabolomics-profiling-workflow/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/nmr-metabolomics-profiling-workflow

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# NMR Metabolomics Profiling

## Summary

End-to-end NMR metabolomics: from raw spectra to identified, quantified metabolites and group-wise statistical comparison.


## When to use

Use when you have NMR metabolomics data (1D/2D spectra or FIDs) and want a quantified, identified metabolite profile — spectral preprocessing (phase/baseline/referencing, binning), metabolite identification by chemical shift, quantification, and group statistics.


## When NOT to use

- The data is not NMR.
- You need a single atomic step, not the full pipeline (use the leaf skill directly via the router).

## Stages

### Stage 1 — preprocess_nmr

**Goal:** NMR spectral preprocessing (phase, baseline, referencing, binning)

**EDAM operation:** operation_3215

**Inputs:** nmr-spectrum · **Outputs:** feature-table, nmr-spectrum

**Candidate leaf skills:** `nmr-spectral-preprocessing-and-phasing` (primary), `nmr-workflow-pipeline-execution`, `nmr-spectra-preprocessing`, `metabolite-dataset-preprocessing`

**Tools (primary):** R, Bioconductor, MWASTools, TopSpin 3.2, Bruker Avance III 600 MHz

**Other candidate tools:** SAND, NMRPipe, NMRBox, PRIMA-Panel

**Grounding:** 3 KB(s); DOIs: 10.1021/acs.analchem.3c03078, 10.1021/acs.analchem.4c04938, 10.1093/bioinformatics/btx477

### Stage 2 — identification

**Goal:** identify metabolites by chemical shift matching

**EDAM operation:** operation_3803

**Inputs:** feature-table · **Outputs:** tsv

**Candidate leaf skills:** `metabolite-peak-assignment-from-nmr` (primary), `nmr-metabolite-identity-confirmation`, `nmr-chemical-shift-interval-matching`, `hmdb-metabolite-query-and-retrieval`

**Tools (primary):** PyTorch, NumPy, Pandas, SciPy, NMRformer

**Other candidate tools:** R, Bioconductor, MWASTools, TopSpin 3.2, openpyxl, XlsxWriter, Python, PyQt5, Human Metabolome Database (HMDB), ROIAL-NMR

**Grounding:** 3 KB(s); DOIs: 10.1002/nbm.70131, 10.1021/acs.analchem.4c05632, 10.1093/bioinformatics/btx477

### Stage 3 — quantification

**Goal:** quantify metabolites from NMR signals

**EDAM operation:** operation_3799

**Inputs:** nmr-spectrum, tsv · **Outputs:** tsv

**Candidate leaf skills:** `nmr-peak-deconvolution` (primary), `compound-abundance-quantification-from-flow`, `nmr-peak-table-generation`

**Tools (primary):** SAND, NMRPipe, NMRBox

**Other candidate tools:** mcfNMR, spec2csv

**Grounding:** 2 KB(s); DOIs: 10.1021/acs.analchem.3c03078, 10.1021/acs.analchem.4c01652

### Stage 4 — statistics

**Goal:** differential analysis of NMR profiles (univariate; multivariate where a leaf exists)

**EDAM operation:** operation_3659

**Inputs:** tsv · **Outputs:** tsv

**Candidate leaf skills:** `multiple-testing-correction-metabolomics` (primary), `confounder-adjustment-epidemiological-analysis`

**Tools (primary):** MWASTools, R, Bioconductor


**Grounding:** 1 KB(s); DOIs: 10.1093/bioinformatics/btx477

## Grounding

Each stage carries the `kb_slugs`/`dois` of the leaves it draws on. Ground any stage against its source paper with the collection's `/ground` command or `bin/perspicacite_kb_bind.py` (Perspicacité KB; serverless local-clone fallback).

## Verification contract

`workflow.yaml` is gradable by `asb solve-workflow` (checkpoint mode). Each stage declares typed outputs; the final stage emits the master deliverable.

## Provenance

Generated by `compose_workflows.py` (semantic binding + EDAM-aware primary selection). `derived_from_workflows` lists ASB per-paper workflows whose structure corroborated this pipeline — the eval-ablation set (SPEC §8). Staging only; promote via `release_gate.py`.